{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:34:03Z","timestamp":1742913243352,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031208676"},{"type":"electronic","value":"9783031208683"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-20868-3_30","type":"book-chapter","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T23:29:12Z","timestamp":1667518152000},"page":"410-423","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-view Stereo Network with\u00a0Attention Thin Volume"],"prefix":"10.1007","author":[{"given":"Zihang","family":"Wan","sequence":"first","affiliation":[]},{"given":"Chao","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Jian","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Zhaopeng","family":"Meng","sequence":"additional","affiliation":[]},{"given":"Jitai","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,4]]},"reference":[{"key":"30_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"30_CR2","doi-asserted-by":"crossref","unstructured":"Ji, M., Gall, J., Zheng, H., Liu, Y., Fang, L.: Surfacenet: an end-to-end 3d neural network for multiview stereopsis. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2307\u20132315 (2017)","DOI":"10.1109\/ICCV.2017.253"},{"key":"30_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1007\/978-3-030-01237-3_47","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y Yao","year":"2018","unstructured":"Yao, Y., Luo, Z., Li, S., Fang, T., Quan, L.: MVSNet: depth inference for unstructured multi-view stereo. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11212, pp. 785\u2013801. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01237-3_47"},{"key":"30_CR4","doi-asserted-by":"crossref","unstructured":"Cheng, S., et al.: Deep stereo using adaptive thin volume representation with uncertainty awareness. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2524\u20132534 (2020)","DOI":"10.1109\/CVPR42600.2020.00260"},{"key":"30_CR5","doi-asserted-by":"crossref","unstructured":"Guo, C., Szemenyei, M., Yi, Y., Wang, W., Chen, B., Fan, C.: Sa-unet: spatial attention u-net for retinal vessel segmentation. In 2020 25th International Conference on Pattern Recognition (ICPR), pp. 1236\u20131242. IEEE, January 2021","DOI":"10.1109\/ICPR48806.2021.9413346"},{"key":"30_CR6","doi-asserted-by":"crossref","unstructured":"Huang, P.H., Matzen, K., Kopf, J., Ahuja, N., Huang, J.B.: Deepmvs: learning multi-view stereopsis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2821\u20132830 (2018)","DOI":"10.1109\/CVPR.2018.00298"},{"key":"30_CR7","doi-asserted-by":"crossref","unstructured":"Luo, K., Guan, T., Ju, L., Huang, H., Luo, Y.: P-mvsnet: learning patch-wise matching confidence aggregation for multi-view stereo. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10452\u201310461 (2019)","DOI":"10.1109\/ICCV.2019.01055"},{"key":"30_CR8","doi-asserted-by":"crossref","unstructured":"Zhang, X., Hu, Y., Wang, H., Cao, X., Zhang, B.: Long-range attention network for multi-view stereo. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 3782\u20133791 (2021)","DOI":"10.1109\/WACV48630.2021.00383"},{"key":"30_CR9","doi-asserted-by":"crossref","unstructured":"Yao, Y., Luo, Z., Li, S., Shen, T., Fang, T., Quan, L.: Recurrent mvsnet for high-resolution multi-view stereo depth inference. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5525\u20135534 (2019)","DOI":"10.1109\/CVPR.2019.00567"},{"key":"30_CR10","doi-asserted-by":"publisher","first-page":"27908","DOI":"10.1109\/ACCESS.2021.3058522","volume":"9","author":"K Zhang","year":"2021","unstructured":"Zhang, K., Liu, M., Zhang, J., Dong, Z.: Pa-mvsnet: sparse-to-dense multi-view stereo with pyramid attention. IEEE Access 9, 27908\u201327915 (2021)","journal-title":"IEEE Access"},{"key":"30_CR11","doi-asserted-by":"crossref","unstructured":"Yang, J., Mao, W., Alvarez, J.M., Liu, M.: Cost volume pyramid based depth inference for multi-view stereo. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4877\u20134886 (2020)","DOI":"10.1109\/CVPR42600.2020.00493"},{"key":"30_CR12","doi-asserted-by":"crossref","unstructured":"Gu, X., Fan, Z., Zhu, S., Dai, Z., Tan, F., Tan, P.: Cascade cost volume for high-resolution multi-view stereo and stereo matching. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2495\u20132504 (2020)","DOI":"10.1109\/CVPR42600.2020.00257"},{"key":"30_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"766","DOI":"10.1007\/978-3-030-58545-7_44","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Hongwei Yi","year":"2020","unstructured":"Yi, Hongwei, Wei, Zizhuang, Ding, Mingyu, Zhang, Runze, Chen, Yisong, Wang, Guoping, Tai, Yu-Wing.: Pyramid multi-view stereo net with self-adaptive view aggregation. In: Vedaldi, Andrea, Bischof, Horst, Brox, Thomas, Frahm, Jan-Michael. (eds.) ECCV 2020. LNCS, vol. 12354, pp. 766\u2013782. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58545-7_44"},{"key":"30_CR14","doi-asserted-by":"crossref","unstructured":"Chen, R., Han, S., Xu, J., Su, H.: Point-based multi-view stereo network. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1538\u20131547 (2019)","DOI":"10.1109\/ICCV.2019.00162"},{"key":"30_CR15","doi-asserted-by":"crossref","unstructured":"Xue, Y., et al.: Mvscrf: learning multi-view stereo with conditional random fields. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4312\u20134321 (2019)","DOI":"10.1109\/ICCV.2019.00441"},{"key":"30_CR16","doi-asserted-by":"crossref","unstructured":"Luo, K., Guan, T., Ju, L., Wang, Y., Chen, Z., Luo, Y.: Attention-aware multi-view stereo. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1590\u20131599 (2020)","DOI":"10.1109\/CVPR42600.2020.00166"},{"key":"30_CR17","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1016\/j.isprsjprs.2021.03.010","volume":"175","author":"A Yu","year":"2021","unstructured":"Yu, A., Guo, W., Liu, B., Chen, X., Wang, X., Cao, X., Jiang, B.: Attention aware cost volume pyramid based multi-view stereo network for 3d reconstruction. ISPRS J. Photogrammetry Remote Sens. 175, 448\u2013460 (2021)","journal-title":"ISPRS J. Photogrammetry Remote Sens."},{"key":"30_CR18","doi-asserted-by":"crossref","unstructured":"Jensen, R., Dahl, A., Vogiatzis, G., Tola, E., Aan\u00e6s, H.: Large scale multi-view stereopsis evaluation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 406\u2013413 (2014)","DOI":"10.1109\/CVPR.2014.59"},{"issue":"4","key":"30_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3072959.3073599","volume":"36","author":"A Knapitsch","year":"2017","unstructured":"Knapitsch, A., Park, J., Zhou, Q.Y., Koltun, V.: Tanks and temples: benchmarking large-scale scene reconstruction. ACM Trans. Graph. (ToG) 36(4), 1\u201313 (2017)","journal-title":"ACM Trans. Graph. (ToG)"},{"key":"30_CR20","doi-asserted-by":"crossref","unstructured":"Galliani, S., Lasinger, K., Schindler, K.: Massively parallel multiview stereopsis by surface normal diffusion. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 873\u2013881 (2015)","DOI":"10.1109\/ICCV.2015.106"},{"key":"30_CR21","unstructured":"Kazhdan, M., Bolitho, M., Hoppe, H.: Poisson surface reconstruction. In Proceedings of the Fourth Eurographics Symposium on Geometry Processing, vol. 7, June 2006"},{"key":"30_CR22","doi-asserted-by":"crossref","unstructured":"Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3273\u20133282 (2019)","DOI":"10.1109\/CVPR.2019.00339"},{"key":"30_CR23","doi-asserted-by":"crossref","unstructured":"Shaw, P., Uszkoreit, J., Vaswani, A.: Self-attention with relative position representations. arXiv preprint arXiv:1803.02155 (2018)","DOI":"10.18653\/v1\/N18-2074"},{"key":"30_CR24","unstructured":"Ramachandran, P., Parmar, N., Vaswani, A., Bello, I., Levskaya, A., Shlens, J.: Stand-alone self-attention in vision models. In: Advances in Neural Information Processing Systems, 32 (2019)"}],"container-title":["Lecture Notes in Computer Science","PRICAI 2022: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20868-3_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T23:42:19Z","timestamp":1667518939000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20868-3_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031208676","9783031208683"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20868-3_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"4 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shangai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pricai.org\/2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"432","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"91","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"39","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"21% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7-8","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"n\/a","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}